Abstract
Visual pattern recognition is a basic capability of many species in nature. The skill of visually recognizing and distinguishing different objects in the surrounding environment gives rise to the development of sensory-motor maps in the brain, with the consequent capability of object reaching and manipulation. This paper presents the implementation of a real-time tracking algorithm for following and evaluating the 3D position of a generic spatial object. The key issue of our approach is the development of a new algorithm for pattern recognition in machine vision, the Least Constrained Square-Fitting of Ellipses (LCSE), which improves the state of the art ellipse fitting procedures. It is a robust and direct method for the least-square fitting of ellipses to scattered data. In this work we applied it to the iCub humanoid robotics platform simulator and real robot. We used it as a base for a circular object localization within the 3D surrounding space. We compared its performance with the Hough Transform and the state of the art ellipse fitting algorithms, in terms of robustness (succes/failure in the object detection) and fitting precision. Our experiments involve robustness against noise, occlusion, and computational complexities analyses.
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